124 أسطر
2.9 KiB
Markdown
124 أسطر
2.9 KiB
Markdown
# Postmortem Report
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## Incident Summary
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**Incident:** Application outage due to repeated OOMKilled events.
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**Duration:** 45 minutes
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**Impact:**
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- The application was unavailable for users.
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- API requests failed during the outage.
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- User experience was significantly affected.
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---
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## Timeline
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| Time | Event |
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|------|-------|
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| 10:00 | Application deployed successfully. |
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| 10:08 | Memory usage started increasing rapidly. |
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| 10:12 | First OOMKilled event occurred. |
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| 10:13 | Kubernetes restarted the container. |
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| 10:15–10:40 | Continuous OOMKilled restart loop. |
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| 10:42 | Engineering team investigated the issue. |
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| 10:45 | Memory limit increased and application stabilized. |
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---
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## Root Cause
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The application exceeded its allocated memory limit.
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The container was repeatedly terminated by Kubernetes with an **OOMKilled** event because memory consumption continued growing beyond the configured limit.
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Possible contributing factors:
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- Memory leak inside the application.
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- Memory limits configured too low.
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- No Horizontal Pod Autoscaler.
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- Lack of memory usage alerts.
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---
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## Resolution
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The engineering team:
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- Increased container memory limits.
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- Restarted the deployment.
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- Verified application health.
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- Monitored memory consumption until stable.
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---
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## Recommendations
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### Immediate Actions
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- Configure appropriate memory requests and limits.
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- Investigate memory leaks.
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- Enable memory monitoring.
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- Configure alerting.
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### Long-Term Improvements
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- Implement Horizontal Pod Autoscaler (HPA).
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- Perform load testing before production.
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- Review memory usage after each deployment.
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- Add automatic scaling policies.
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- Conduct regular post-deployment monitoring.
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---
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# Auto-Scaling Policy
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To prevent similar incidents, the platform should implement:
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- Minimum replicas: **2**
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- Maximum replicas: **10**
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- Scale out when:
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- Memory usage > 70%
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- CPU usage > 70%
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- Scale in when:
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- Memory usage < 40%
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- CPU usage < 40%
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- Cooldown period: **5 minutes**
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- Enable automatic replacement of unhealthy pods.
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This policy ensures enough capacity during traffic spikes while reducing resource waste during normal operation.
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---
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# Early Detection Using Ghaymah Monitoring
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The issue can be detected early by monitoring:
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- Memory usage
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- CPU usage
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- Container restart count
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- OOMKilled events
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- Pod health status
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- Response time
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- Error rate
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Recommended alerts:
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- Memory usage above 80%
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- More than 3 container restarts within 10 minutes
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- Pod enters CrashLoopBackOff
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- Health endpoint becomes unavailable
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- Response time exceeds acceptable thresholds
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These monitoring practices allow engineers to respond before users experience service interruption.
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---
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# Lessons Learned
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- Proper resource limits are essential.
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- Continuous monitoring is critical.
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- Autoscaling improves application availability.
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- Early alerting reduces downtime.
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- Capacity planning should be reviewed before production deployments. |